Recent studies have investigated combinations of SFL metrics or sources of information for use in fault localization. Xuan et al. [1]} presented a technique that uses learning-to-rank methods [2]} to combine multiple SFL metrics. Sohn and Yoo [3]} combined SFL results with source code metrics from static analysis. Zou et al. [4]} found that combining a variety of SFL techniques was beneficial. Li et al. [5]} uses deep learning with neural networks to integrate multiple sources of information that vary from SFL metrics to textual similarity measures. Although these approaches to combining different sources of information in fault localization are promising, they do not adjust for confounding or other biases and therefore their results are prone to bias.[6]}
Mutation-based SFL techniques [7]}, [8]}, [9]} measure the suspiciousness of a program statement by how much a mutation to it can change the number of program failures that occur on a test set. These techniques often generate a very large number of mutations, hence they may be very costly to apply. Unlike UniVal, they do not employ established causal inference methodology.
